

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AlignProcessor, EfficientNetImageProcessor



@require_vision
class        A__(unittest.TestCase							):
						"""simple docstring"""






						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						int 							= tempfile.mkdtemp()

									a_						:						Any 							= [
									    """[UNK]""",
									    """[CLS]""",
									    """[SEP]""",
									    """[PAD]""",
									    """[MASK]""",
									    """want""",
									    """##want""",
									    """##ed""",
									    """wa""",
									    """un""",
									    """runn""",
									    """##ing""",
									    """,""",
									    """low""",
									    """lowest""",
									]
									a_						:						Any 							= os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES["""vocab_file"""]    )
									with open(self.vocab_file  ,      """w"""  ,      encoding="""utf-8"""    ) as vocab_writer:
												vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]    )    )

									a_						:						str 							= {
									    """do_resize""": True,
									    """size""": 20,
									    """do_center_crop""": True,
									    """crop_size""": 18,
									    """do_normalize""": True,
									    """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
									    """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
									}
									a_						:						Optional[int] 							= os.path.join(self.tmpdirname  ,      _lowercase    )
									with open(self.image_processor_file  ,      """w"""  ,      encoding="""utf-8"""    ) as fp:
												json.dump(_lowercase  ,      _lowercase    )





						def 			UpperCamelCase__					(    self  ,      **_lowercase    )     -> Optional[Any]:
									return BertTokenizer.from_pretrained(self.tmpdirname  ,      **_lowercase    )





						def 			UpperCamelCase__					(    self  ,      **_lowercase    )     -> Union[str, Any]:
									return BertTokenizerFast.from_pretrained(self.tmpdirname  ,      **_lowercase    )





						def 			UpperCamelCase__					(    self  ,      **_lowercase    )     -> str:
									return EfficientNetImageProcessor.from_pretrained(self.tmpdirname  ,      **_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									shutil.rmtree(self.tmpdirname    )





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Union[str, Any] 							= [np.random.randint(255  ,      size=(3, 30, 400)  ,      dtype=np.uinta    )]
									a_						:						Tuple 							= [Image.fromarray(np.moveaxis(_lowercase  ,      0  ,      -1    )    ) for x in image_inputs]
									return image_inputs





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									a_						:						List[str] 							= self.get_tokenizer()
									a_						:						int 							= self.get_rust_tokenizer()
									a_						:						Union[str, Any] 							= self.get_image_processor()

									a_						:						int 							= AlignProcessor(tokenizer=_lowercase  ,      image_processor=_lowercase    )
									processor_slow.save_pretrained(self.tmpdirname    )
									a_						:						Dict 							= AlignProcessor.from_pretrained(self.tmpdirname  ,      use_fast=_lowercase    )

									a_						:						Dict 							= AlignProcessor(tokenizer=_lowercase  ,      image_processor=_lowercase    )
									processor_fast.save_pretrained(self.tmpdirname    )
									a_						:						int 							= AlignProcessor.from_pretrained(self.tmpdirname    )

									self.assertEqual(processor_slow.tokenizer.get_vocab()  ,      tokenizer_slow.get_vocab()    )
									self.assertEqual(processor_fast.tokenizer.get_vocab()  ,      tokenizer_fast.get_vocab()    )
									self.assertEqual(tokenizer_slow.get_vocab()  ,      tokenizer_fast.get_vocab()    )
									self.assertIsInstance(processor_slow.tokenizer  ,      _lowercase    )
									self.assertIsInstance(processor_fast.tokenizer  ,      _lowercase    )

									self.assertEqual(processor_slow.image_processor.to_json_string()  ,      image_processor.to_json_string()    )
									self.assertEqual(processor_fast.image_processor.to_json_string()  ,      image_processor.to_json_string()    )
									self.assertIsInstance(processor_slow.image_processor  ,      _lowercase    )
									self.assertIsInstance(processor_fast.image_processor  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Any:
									a_						:						Optional[int] 							= AlignProcessor(tokenizer=self.get_tokenizer()  ,      image_processor=self.get_image_processor()    )
									processor.save_pretrained(self.tmpdirname    )

									a_						:						Tuple 							= self.get_tokenizer(bos_token="""(BOS)"""  ,      eos_token="""(EOS)"""    )
									a_						:						Any 							= self.get_image_processor(do_normalize=_lowercase  ,      padding_value=1.0    )

									a_						:						Optional[int] 							= AlignProcessor.from_pretrained(
									    self.tmpdirname  ,      bos_token="""(BOS)"""  ,      eos_token="""(EOS)"""  ,      do_normalize=_lowercase  ,      padding_value=1.0    )

									self.assertEqual(processor.tokenizer.get_vocab()  ,      tokenizer_add_kwargs.get_vocab()    )
									self.assertIsInstance(processor.tokenizer  ,      _lowercase    )

									self.assertEqual(processor.image_processor.to_json_string()  ,      image_processor_add_kwargs.to_json_string()    )
									self.assertIsInstance(processor.image_processor  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						Union[str, Any] 							= self.get_image_processor()
									a_						:						str 							= self.get_tokenizer()

									a_						:						List[Any] 							= AlignProcessor(tokenizer=_lowercase  ,      image_processor=_lowercase    )

									a_						:						int 							= self.prepare_image_inputs()

									a_						:						Optional[int] 							= image_processor(_lowercase  ,      return_tensors="""np"""    )
									a_						:						List[str] 							= processor(images=_lowercase  ,      return_tensors="""np"""    )

									for key in input_image_proc.keys():
												self.assertAlmostEqual(input_image_proc[key].sum()  ,      input_processor[key].sum()  ,      delta=1e-2    )





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									a_						:						Dict 							= self.get_image_processor()
									a_						:						int 							= self.get_tokenizer()

									a_						:						Optional[int] 							= AlignProcessor(tokenizer=_lowercase  ,      image_processor=_lowercase    )

									a_						:						Optional[int] 							= """lower newer"""

									a_						:						List[str] 							= processor(text=_lowercase    )

									a_						:						Any 							= tokenizer(_lowercase  ,      padding="""max_length"""  ,      max_length=64    )

									for key in encoded_tok.keys():
												self.assertListEqual(encoded_tok[key]  ,      encoded_processor[key]    )





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						List[str] 							= self.get_image_processor()
									a_						:						Any 							= self.get_tokenizer()

									a_						:						int 							= AlignProcessor(tokenizer=_lowercase  ,      image_processor=_lowercase    )

									a_						:						Optional[Any] 							= """lower newer"""
									a_						:						Optional[int] 							= self.prepare_image_inputs()

									a_						:						List[str] 							= processor(text=_lowercase  ,      images=_lowercase    )

									self.assertListEqual(list(inputs.keys()    )  ,      ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]    )

									# test if it raises when no input is passed
									with pytest.raises(_lowercase    ):
												processor()





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Union[str, Any] 							= self.get_image_processor()
									a_						:						List[Any] 							= self.get_tokenizer()

									a_						:						List[str] 							= AlignProcessor(tokenizer=_lowercase  ,      image_processor=_lowercase    )

									a_						:						Union[str, Any] 							= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									a_						:						Optional[int] 							= processor.batch_decode(_lowercase    )
									a_						:						Optional[int] 							= tokenizer.batch_decode(_lowercase    )

									self.assertListEqual(_lowercase  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Dict 							= self.get_image_processor()
									a_						:						Dict 							= self.get_tokenizer()

									a_						:						Optional[int] 							= AlignProcessor(tokenizer=_lowercase  ,      image_processor=_lowercase    )

									a_						:						int 							= """lower newer"""
									a_						:						Dict 							= self.prepare_image_inputs()

									a_						:						List[str] 							= processor(text=_lowercase  ,      images=_lowercase    )

									self.assertListEqual(list(inputs.keys()    )  ,      processor.model_input_names    )





def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			return x if y == 0 else greatest_common_divisor(a__  ,	x % y)


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			return (x * y) // greatest_common_divisor(a__  ,	a__)


def 					_UpperCAmelCase				(						a__ = 2_0):

			'''simple docstring'''



			a_						:						Any 							= 1
			for i in range(1  ,	n + 1):
						a_						:						Tuple 							= lcm(a__  ,	a__)
			return g


if __name__ == "__main__":
	print(F"""{solution() = }""")


